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Creating Reusable Python Code: From Notebooks to Scripts to Packages
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Details
The popularity of Python stems in large part from its convenience for creating quick data analyses in Jupyter notebooks. This workshop will explore how to extend code in such notebooks to make it easier for your colleagues to validate and extend your analyses. We will cover writing simple scripts as well as more complicated tools with command-line interfaces. Time allowing, we will also discuss the benefits of grouping related scripts into packages, and introduce the basics of structuring packages using object-oriented design.
Knowledge prerequisites: Basic knowledge of Python
Hardware/software prerequisites: This workshop will provide example code in a GitHub repository. To work with the examples, particpants will need run Python code. This can be done using the Python installation on a laptop or by using the Adroit cluster. Please request an account on Adroit at least one hour before the workshop.
Workshop format: Demonstration and hands-on
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Speakers
Michal Grzadkowski
Michal joined Princeton Research Computing in 2021 after five years working as a Research Software Engineer at Oregon Health & Science University, where his primary project involved studying the application of machine learning models to better understand the impacts of mutations commonly implicated in tumorigenesis. This involved implementing novel methods for representing the taxonomies of mutations present in cancer cohorts, as well as developing software for deploying and consolidating thousands of classification models on a high-performance compute cluster. His present work focuses on optimizing pipelines for generating quantitative assessments of the contributions various types of assets can make to a power grid’s ability to satisfy the demand for electricity over a given time frame.
Hosted By
Co-hosted with: GradFUTURES
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